Reconstruction of gene networks using Bayesian learning and manipulation experiments

Abstract

MOTIVATION The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data… (More)
DOI: 10.1093/bioinformatics/bth337

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@article{Pournara2004ReconstructionOG, title={Reconstruction of gene networks using Bayesian learning and manipulation experiments}, author={Iosifina Pournara and Lorenz Wernisch}, journal={Bioinformatics}, year={2004}, volume={20 17}, pages={2934-42} }